def historic_run():
length = 400
price = price_history.get_prices()[-length:]
quick_ma_f = decisions.sma_gen(9)
slow_ma_f = decisions.sma_gen(24)
open_percent = 0.8
close_percent = 0.5
t, money, position, close_t, close_profit_loss_rate = simulate(price, quick_ma=quick_ma_f, slow_ma=slow_ma_f, open_threshold_ratio=open_percent/100, close_threshold_ratio=close_percent/100)
quick_ma = moving_average_values(price, quick_ma_f)
slow_ma = moving_average_values(price, slow_ma_f)
matplotlib.style.use('ggplot')
fig, (p1, p2) = plt.subplots(2, 1, sharex=True)
p1.plot(t, price, 'k-', linewidth=2)
p1.plot(t, quick_ma, 'r-', linewidth=1)
p1.plot(t, slow_ma, 'g-', linewidth=1)
p1.add_collection(MpCollections.BrokenBarHCollection.span_where(t, ymin=0, ymax=10000, where=position > 0, facecolor='green', alpha=0.1))
p1.add_collection(MpCollections.BrokenBarHCollection.span_where(t, ymin=0, ymax=10000, where=position < 0, facecolor='red', alpha=0.1))
# p2_1 = p2.twinx()
p2.plot(close_t, money, 'b-')
money_prev = [1] + money[:-1]
p2.bar(
close_t,
(np.array(close_profit_loss_rate) - 1) * money_prev,
bottom=money_prev,
width=10,
color=['r' if pl < 1 else 'g' for pl in close_profit_loss_rate],
alpha=1)
plt.show()
def tune_run():
length = 400
y_range = x_range = np.linspace(0.01, 1.5, 10)
prices = price_history.get_prices()[-length:]
x, y, gains, losses = zip(
*[
_tuple(
x,
y,
*simulate_money(
prices,
quick_ma=decisions.sma_gen(quick_sma),
slow_ma=decisions.sma_gen(slow_sma),
open_threshold_ratio=open_percent / 100,
close_threshold_ratio=close_percent / 100,
)
)
for x in x_range
for y in y_range
for open_percent in [x]
for close_percent in [y]
for quick_sma in [9]
for slow_sma in [24]
if quick_sma < slow_sma
if close_percent <= open_percent
]
)
matplotlib.style.use("ggplot")
fig, (p1) = plt.subplots(1, 1, sharex=True)
for x_, y_, z_ in zip(x, y, gains):
p1.scatter([x_], [y_], s=(z_ * 1000))
for x_, y_, z_ in zip(x, y, losses):
p1.scatter([x_], [y_], s=(z_ * 1000), c="r", alpha=0.5)
for x_, y_, gain, loss in zip(x, y, gains, losses):
p1.annotate(
# '+{}%\n-{}%\n={}%'.format(int(gain * 100), int(loss * 100), int((gain - loss) * 100)),
"*{}".format(round(gain * loss, 1)),
(x_, y_),
color="w",
horizontalalignment="center",
verticalalignment="center",
fontweight="bold",
)
plt.show()
